A man with localized prostate cancer faces a bewildering constellation of treatment options. Most of these options provide similar chances of survival, so the optimal choice for any particular individual is crucially dependant on the strength of his preferences for various clinical and non-clinical treatment characteristics. Helping these men identify their optimal treatment choice will reduce the burden of this illness on these men, part of the mission of the NIH. In research, measuring the strength of these preferences has traditionally been approached with several methods of "utility assessment." Unfortunately, each of these existing methods has significant limitations, ranging from basic concerns about validity to practical concerns regarding the cognitive burden placed on patients, which have presented barriers to their clinical use. Investigators in Marketing have also faced the challenge of characterizing individual preferences, albeit for goods and services, when designing new consumer products. In Marketing, "conjoint analysis" has emerged as a powerful methodology with which to characterize consumer preferences and to predict consumer purchasing behavior. Given its simplicity and predictive validity, this method may be a solution to limitations of existing preference assessment methods. We believe that conjoint analysis has potential to improve decisions in clinical settings. Our primary hypotheses are twofold. First, we hypothesize that conjoint analysis-based preference assessment and educational support will improve decision quality in men with prostate cancer as compared to educational support alone. Second, we hypothesize that conjoint analysis has superior construct validity in measuring the preferences of men with prostate cancer compared to traditional methods of preference assessment, specifically time trade-off and rating scale methods. To test these hypotheses, we will undertake two randomized controlled studies. This will be done by first recruiting men who have been treated for prostate cancer and identifying treatment attributes of importance to them. Based on these data, we will use established software platforms to develop preference assessment applications for conjoint analysis-based and traditional assessment methods, to be used in the randomized trials. One randomized trial using the conjoint analysis application will examine metrics of decision quality in men who undergo conjoint analysis, along with an educational intervention, compared with decision quality in men who undergo the educational intervention only. The second will compare criterion validity of these applications using metrics published in the literature This work is guided by the promise of eventually deploying point-of-care decision aids to improve the quality of decision making for men with prostate cancer. Public Health Relevance: This research aims to improve our ability to individualize prostate cancer care and improve decision quality for men with this disease. We will test whether a method taken from consumer marketing research can more accurately identify individual patients'values better than existing methods used in healthcare research. We will test whether use of this method improves the quality of the treatment decisions that are made by patients.